Base Case Type A 2HL¶
Monte Carlo run
Iterations: 1000
epochs: 150
batch_size: 100
Optimizer: SGD. Learning Rate: 0.01
Data is not shuffled at each iteration
Opened data located in data/TypeA
Num classes: 2
Total Samples: 20000
Vector size: 128
Train percentage: 80%
Models:
Complex Network
Dense layer:
input size = 128(<class ‘numpy.complex64’>) -> output size = 100;
act_fun = cart_relu;
weight init = Glorot Uniform; bias init = Zeros
Dropout: 0.5
Dense layer:
input size = 100(complex64) -> output size = 40;
act_fun = cart_relu;
weight init = Glorot Uniform; bias init = Zeros
Dropout: 0.5
Dense layer:
input size = 40(complex64) -> output size = 2;
act_fun = softmax_real;
weight init = Glorot Uniform; bias init = Zeros
Dropout: None
Real Network
Dense layer:
input size = 256(<class ‘numpy.float32’>) -> output size = 200;
act_fun = cart_relu;
weight init = Glorot Uniform; bias init = Zeros
Dropout: 0.5
Dense layer:
input size = 200(<class ‘numpy.float32’>) -> output size = 80;
act_fun = cart_relu;
weight init = Glorot Uniform; bias init = Zeros
Dropout: 0.5
Dense layer:
input size = 80(<class ‘numpy.float32’>) -> output size = 2;
act_fun = softmax_real;
weight init = Glorot Uniform; bias init = Zeros
Dropout: None